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Do Smart Adaptive Systems Exist? : Best Practice for Selection and Combination of Intelligent Methods
This book is intended as a reference and a guide summarising and focusing on best practices when using intelligent techniques and building systems requiring a degree of adaptation and intelligence. Another attractive feature of the book is that it brings together experts from neural network, fuzzy, machine learning, evolutionary and hybrid systems communities who will provide their views on how these different intelligent technologies have contributed and will contribute to creation of smart adaptive systems of the future.
Distributed computing and internet technology ; 17th International Conference, ICDCIT 2021, Bhubaneswar, India, January 7–10, 2021, Proceedings
This book constitutes the proceedings of the 17th International Conference on Distributed Computing and Internet Technology, ICDCIT 2020, held in Bhubaneswar, India, in January 2021. The 13 full papers presented together with 4 short papers were carefully reviewed and selected from 99 submissions. The papers were organized in topical sections named: invited talks, cloud computing and networks, distributed algorithms, concurrency and parallelism, graph algorithms and security, social networks and machine learning, and short papers.
Distributed artificial intelligence ; 2nd International conference, DAI 2020, Nanjing, China, October 24–27, 2020, Proceedings
This book constitutes the refereed proceedings of the Second International Conference on Distributed Artificial Intelligence, DAI 2020, held in Nanjing, China, in October 2020. The 9 full papers presented in this book were carefully reviewed and selected from 22 submissions. DAI aims at bringing together international researchers and practitioners in related areas including general AI, multiagent systems, distributed learning, computational game theory, etc., to provide a single, high-profile, internationally renowned forum for research in the theory and practice of distributed AI.
Disruptive trends in automation technology
The industrial sector is being transformed by the convergence of information technology and operational technology. The latter is another name for automation technology and covers established systems such as supervisory control and data acquisition (SCADA), programmable logic controllers (PLC), fieldbuses, and automation and control systems. As this technology is connected to the Internet and 5G networks, some monitoring, control, and analytic functionalities are deployed to the edge or cloud, and researchers are challenged to ensure the security, dependability, real-time performance, and maintainability of the resulting systems. The big data that is accessible from these systems create opportunities for artificial intelligence applications that can further disrupt the established practices in the automation domain.
Disrupting Finance : FinTech and Strategy in the 21st Century
This Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry.
Discovery Science ; Vol. 4265 ; 9th International Conference, DS 2006, Barcelona, Spain, October 7-10, 2006, Proceedings
This LNAI volume, containing the proceedings of the 9th International C- ference onDiscoveryScience, is structured in three parts. The ?rstpart contains the papers/abstracts of the invited talks, the second part contains the accepted long papers, and the third part the accepted regular (short) papers. Out of 87 submitted papers, 23 were accepted for publication as long papers, and 18 as regular papers. All the submitted papers were reviewed by two or three ref- ees. In addition to the presentations of accepted papers, the DS 2006 conference program consisted of three invited talks, two tutorials, the collocated ALT 2006 conference and the Pascal Dialogues workshop.
Discovery science ; Vol. 3735 ; 8th International Conference, DS 2005, Singapore, October 8-11, 2005, Proceedings
This book constitutes the refereed proceedings of the 8th International Conference on Discovery Science, DS 2005, held in Singapore in October 2005, co-located with the International Conference on Algorithmic Learning Theory (ALT 2005). The 21 revised long papers and the 6 revised regular papers presented together with 9 project reports and 5 invited papers were carefully reviewed and selected from 112 submissions. The papers cover all issues in the area of automating scientific discovery or working on tools for supporting the human process of discovery in science.
Discovery Science ; 11th International Conference, DS 2008, Budapest, Hungary, October 13-16, 2008. Proceedings
This book constitutes the refereed proceedings of the 11th International Conference on Discovery Science, DS 2008, held in Budapest, Hungary, in October 2008, co-located with the 19th International Conference on Algorithmic Learning Theory, ALT 2008.
Discovery Science ; 10th International Conference, DS 2007 Sendai, Japan, October 1-4, 2007. Proceedings
This volume consists of three parts. The first part contains the papers/abstracts of the invited talks, the second part contains the accepted long papers, and the third part contains the accepted regular papers.
Digitization of healthcare data using blockchain
Gives a detailed description of the integration of blockchain technology for Electronic Health Records and provides the research challenges to consider in various disciplines such as supply chain, drug discovery, and data management. he aim of the book is to investigate the concepts of blockchain technology and its association with the recent development and advancements in the medical field. Moreover, it focuses on the integration of workflow strategies like NLP, and AI which could be adopted for boosting the clinical documentation and electronic healthcare records (EHR) usage by bringing down the physician EHR data entry. Also, the book covers the usage of smart contracts for securing patient records. Digitization of Healthcare Data Using Blockchain presents the practical implementations that deal with developing a web framework for building highly usable healthcare applications, a simple blockchain-powered EHR system.
Digital twin : Architectures, networks, and applications
Offers comprehensive, self-contained knowledge on digital twin (DT), which is a very promising technology for achieving digital intelligence in the next-generation wireless communications and computing networks. DT is a key technology to connect physical systems and digital spaces in Metaverse. The objectives of this book are to provide the basic concepts of DT, to explore the promising applications of DT integrated with emerging technologies, and to give insights into the possible future directions of DT. For easy understanding, this book also presents several use cases for DT models and applications in different scenarios. The book starts with the basic concepts, models, and network architectures of DT. Then, we present the new opportunities when DT meets edge computing, Blockchain and Artificial Intelligence, and distributed machine learning (e.g., federated learning, multi-agent deep reinforcement learning).
Digital innovation management : People, process, platforms and policy
Shows how digital innovation management practices can facilitate project coordination aimed at business success while prioritizing environmental and social responsibility. structured around the four pillars of digital innovation (people, process, platforms, and policy), the book illustrates how digital and physical elements of innovation management can be integrated to create new marketing offerings, organizational processes, and business models that align with sustainability and human centric ideals. the book supports a theoretical framing of digital innovation management with case studies of creative digital ecosystems such as smart cities, which leverage integrated digital systems to advance research, innovation, and education. finally, the book provides an analysis of emerging technologies, such as artificial intelligence and blockchain, that may introduce significant digital innovations to make smarter organizations and territories.
Digital human modeling and applications in health, safety, ergonomics and risk management : Posture, motion and health ; 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part I
This two-volume set LNCS 12198 and 12199 constitutes the thoroughly refereed proceedings of the 11th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2020, which was supposed to be held as part of the 22st HCI International Conference, HCII 2020, in Copenhagen, Denmark, in July 2020. The conference was held virtually due to the COVID-19 pandemic.
Digital human modeling and applications in health, safety, ergonomics and risk management : Human communication, organization and work ; 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part II
This two-volume set LNCS 12198 and 12199 constitutes the thoroughly refereed proceedings of the 11th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2020, which was supposed to be held as part of the 22st HCI International Conference, HCII 2020, in Copenhagen, Denmark, in July 2020. The conference was held virtually due to the COVID-19 pandemic.
Deterministic and statistical methods in Machine Learning ; 1st International Workshop, Sheffield, UK, September 7-10, 2004. Revised Lectures
This book consitutes the refereed proceedings of the First International Workshop on Machine Learning held in Sheffield, UK, in September 2004. The 19 revised full papers presented were carefully reviewed and selected for inclusion in the book. They address all current issues in the rapidly maturing field of machine learning that aims to provide practical methods for data discovery, categorisation and modelling. The particular focus of the workshop was advanced research methods in machine learning and statistical signal processing.
Designing machine learning systems : An iterative process for production-ready applications
Machine learning systems are both complex and unique. Each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. The book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems
Design computing and cognition 06 ; 1st ed. ; Proceedings of the 2nd International conference on design computing and cognition
This is the second volume of the new conference series Design Computing and Cognition (DCC) that takes over from and subsumes the successful series Artificial Intelligence in Design (AID) published by Kluwer since 1992. The AID volumes have become standard reference texts for the field. It is expected that the DCC volumes will perform the same role.
Design and Analysis of Learning Classifier Systems : A Probabilistic Approach
This book provides a comprehensive introduction to the design and analysis of Learning Classifier Systems (LCS) from the perspective of machine learning. LCS are a family of methods for handling unsupervised learning, supervised learning and sequential decision tasks by decomposing larger problem spaces into easy-to-handle subproblems.
Deepfake detection
The rise of large language models (LLMs) and the increasing sophistication of deepfake images have made detecting synthetic content a pressing challenge. Several approaches have been proposed to tackle this problem, including statistical analysis, and machine learning algorithms. In this project, A novel zero-shot approach is proposed that utilizes the power of LLMs to detect fake text. The pre-trained LLM is fine-tuned to enhance its ability to differentiate real and fake text. The approach uses the LLM to detect text by analyzing the log probabilities of the text. For detecting fake images, computer vision algorithms and neural networks are used to analyze facial features. The facial region is cropped and preprocessed and the neural network identifies patterns indicative of synthetic content.
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety
Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
